Explore the recent global developments with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## ── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Look at the data

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita. By running head we will get the first six rows.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

We see an interesting spread with an outlier to the right. Answer the following questions, please:

Q1. Why does it make sense to have a log10 scale on x axis? When the data covers a wide range of values it can be useful to use the log10 to scale the axes so that we do not just get a continuous scale.

Q2. What country is the richest in 1952 (far right on x axis)? Kuwait. I figured this out in the following:

gapminder %>% 
  select(country, year, gdpPercap) %>% #here I select that I want to see country, year and gdpPercap
  filter(year == "1952") %>% #selecting that I only want the year 1952 to be shown
  arrange(desc(gdpPercap)) #arranging the order of gdpPercap to be descending
## # A tibble: 142 x 3
##    country         year gdpPercap
##    <fct>          <int>     <dbl>
##  1 Kuwait          1952   108382.
##  2 Switzerland     1952    14734.
##  3 United States   1952    13990.
##  4 Canada          1952    11367.
##  5 New Zealand     1952    10557.
##  6 Norway          1952    10095.
##  7 Australia       1952    10040.
##  8 United Kingdom  1952     9980.
##  9 Bahrain         1952     9867.
## 10 Denmark         1952     9692.
## # … with 132 more rows

You can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  geom_point(aes(color = continent)) + #changing the color of the points according to continent
  xlab("GDP Per Capita") + #changing the name of the x axis
  ylab("Life Expectancy") #changing the name of the y axis

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Q3. Can you differentiate the continents by color and fix the axis labels? Yes I did it (see the above)

Q4. What are the five richest countries in the world in 2007? Norway, Kuwait, Singapore, United States and Ireland. I figured it out by doing the following:

gapminder %>% 
  select(country, year, gdpPercap) %>% #here I select that I want to see country, year and gdpPercap
  filter(year == "2007") %>% #selecting that I only want the year 2007 to be shown
  arrange(desc(gdpPercap)) #arranging the order of gdpPercap to be descending
## # A tibble: 142 x 3
##    country           year gdpPercap
##    <fct>            <int>     <dbl>
##  1 Norway            2007    49357.
##  2 Kuwait            2007    47307.
##  3 Singapore         2007    47143.
##  4 United States     2007    42952.
##  5 Ireland           2007    40676.
##  6 Hong Kong, China  2007    39725.
##  7 Switzerland       2007    37506.
##  8 Netherlands       2007    36798.
##  9 Canada            2007    36319.
## 10 Iceland           2007    36181.
## # … with 132 more rows

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.

library(gifski)
anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]s I am going to add a title to the last of the animations, the one called anim2, by using the labs (labels) function and defining the title to be year.

anim2 +
  labs(title = "year: {frame_time}") + #making the year change in sync with the animation
  geom_point(aes(color = continent)) #coloring the animation by continent

Q6 Can you make the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]

I change the labels on the axes by disabling scientific notation:

  options(scipen = 999) #disabling scientific notation, options changes how R visualizes data

Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

How is the life expectancy evolving in Denmark through time?

DK <- gapminder %>% 
  filter(country == "Denmark") #defining DK
DK
## # A tibble: 12 x 6
##    country continent  year lifeExp     pop gdpPercap
##    <fct>   <fct>     <int>   <dbl>   <int>     <dbl>
##  1 Denmark Europe     1952    70.8 4334000     9692.
##  2 Denmark Europe     1957    71.8 4487831    11100.
##  3 Denmark Europe     1962    72.4 4646899    13583.
##  4 Denmark Europe     1967    73.0 4838800    15937.
##  5 Denmark Europe     1972    73.5 4991596    18866.
##  6 Denmark Europe     1977    74.7 5088419    20423.
##  7 Denmark Europe     1982    74.6 5117810    21688.
##  8 Denmark Europe     1987    74.8 5127024    25116.
##  9 Denmark Europe     1992    75.3 5171393    26407.
## 10 Denmark Europe     1997    76.1 5283663    29804.
## 11 Denmark Europe     2002    77.2 5374693    32167.
## 12 Denmark Europe     2007    78.3 5468120    35278.
ggplot(DK, aes(gdpPercap, lifeExp, group = country)) + #plotting gdp in x and lifeexp in y, grouping by country
  geom_line() + #making the visualization a line
  scale_color_viridis_d() +
  labs(x = "GDP", y = "Life Expectancy") + #defining x and y
  transition_reveal(year) #making it move

From the visualization we can see that the life expectancy in Denmark generally gets higher through the years and so does the GDP.